Parallelization and scatter-gather patterns
Many advanced reasoning and generation tasks – such as summarizing large documents, evaluating multiple solution paths, or comparing diverse perspectives – benefit from the parallel execution of prompts. Traditional sequential workflows fall short when scalability, responsiveness, and fault tolerance are required. To overcome this, LLM-based parallelization can be reimagined using an event-driven scatter-gather pattern, where tasks are dynamically fanned out to autonomous agents and results intelligently synthesized.
The following diagram is an example of an LLM parallelization workflow:
Scatter-gather
In distributed systems, a scatter-gather pattern sends tasks to multiple services or processing units in parallel, waits for their responses, and then aggregates results into a consolidated output. Unlike fan-out, scatter-gather is coordinated because it expects responses and usually applies logic to combine, compare, and select results.
Common implementations for parallelization and scatter-gather include the following:
AWS Step Functions map a state for parallel task execution
AWS Lambda with concurrency, coordinating results from multiple invoked functions
Amazon EventBridge with correlation IDs and aggregation workflows
Custom controller pattern to manage fan-out and gather results by using Amazon Simple Storage Service (Amazon S3), Amazon DynamoDB, or queues
The following diagram is an AWS architectural example of scatter-gather:
A user sends a request to a central coordinator function that scatters the task by publishing parallel messages to an Amazon Simple Notification Service (Amazon SNS) topic.
Each message includes task metadata and is routed to a specialized worker AWS Lambda.
Each worker AWS Lambda independently processes its assigned subtask (for example, querying an external API, processing a document, and analyzing data).
Results are written to a common storage layer, such as Amazon Simple Queue Service (Amazon SQS).
The aggregator function waits for all responses to be completed, and then it does the following:
Gathers and aggregates the results (for example, merges summaries, selects best matches)
Sends a final response or triggers a downstream workflow
Common use cases for scatter-gather patterns include the following:
Federated search
Price comparison engines
Aggregated data analysis
Multimodel inference
LLM-based parallelization (scatter-gather cognition)
In agentic systems, parallelization closely mirrors scatter-gather by distributing subtasks across multiple LLM calls or agents, each independently reasoning through a portion of the problem. Returned results are gathered and synthesized by an aggregation process, which is often another LLM or controller agent.
Agent parallelization
An agent submits a request "Summarize insights across these 10 reports."
It scatters the reports to 10 parallel LLM summarization tasks.
When it returns all summaries, the agent does the following:
Aggregates summaries into a unified briefing
Identifies themes or contradictions
Sends the synthesized output to the user
This agentic workflow enables scalable, modular, and adaptive parallel reasoning. This is ideal for use cases that require high cognitive throughput.
The following diagram is an AWS architectural example of agent parallelization:
A user submits a multipart query or document set.
A controller AWS Lambda or step function distributes the subtasks. Each task invokes an Amazon Bedrock LLM call or subagent with its own prompt.
When the calls and subtasks are complete, results are stored (for example, in Amazon S3 or memory store), and an aggregation step merges, compares, or filters the outputs.
The system returns the final response to the user or downstream agent.
This system has a distributed reasoning loop with traceability, fault tolerance, and optional result weighting or selection logic.
Takeaways
Agentic parallelization uses scatter-gather patterns to distribute LLM tasks, enabling parallel processing and intelligent result synthesis.
